In [1]:
from ydata_profiling import ProfileReport
import pandas as pd 
In [2]:
df = pd.read_csv("data/NewYork_data.tsv", sep= '\t')

print(df)
C:\Users\kaush\AppData\Local\Temp\ipykernel_4144\3817430330.py:1: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.
  df = pd.read_csv("data/NewYork_data.tsv", sep= '\t')
         CRASH DATE CRASH TIME   BOROUGH ZIP CODE   LATITUDE  LONGITUDE  \
0        09/11/2021       2:39       NaN      NaN        NaN        NaN   
1        03/26/2022      11:45       NaN      NaN        NaN        NaN   
2        06/29/2022       6:55       NaN      NaN        NaN        NaN   
3        09/11/2021       9:35  BROOKLYN  11208.0  40.667202 -73.866500   
4        12/14/2021       8:13  BROOKLYN  11233.0  40.683304 -73.917274   
...             ...        ...       ...      ...        ...        ...   
2075422  03/05/2024      17:22    QUEENS  11436.0  40.680477 -73.792100   
2075423  03/05/2024      17:00  BROOKLYN  11204.0  40.610786 -73.978820   
2075424  03/03/2024      17:50       NaN      NaN  40.675053 -73.947235   
2075425  03/05/2024      14:30  BROOKLYN  11207.0  40.677900 -73.892586   
2075426  03/05/2024       8:00    QUEENS  11385.0  40.706512 -73.878136   

                        LOCATION           ON STREET NAME CROSS STREET NAME  \
0                            NaN    WHITESTONE EXPRESSWAY         20 AVENUE   
1                            NaN  QUEENSBORO BRIDGE UPPER               NaN   
2                            NaN       THROGS NECK BRIDGE               NaN   
3          (40.667202, -73.8665)                      NaN               NaN   
4        (40.683304, -73.917274)          SARATOGA AVENUE    DECATUR STREET   
...                          ...                      ...               ...   
2075422    (40.680477, -73.7921)        SUTPHIN BOULEVARD    FOCH BOULEVARD   
2075423   (40.610786, -73.97882)                      NaN               NaN   
2075424  (40.675053, -73.947235)       SAINT MARKS AVENUE               NaN   
2075425    (40.6779, -73.892586)            MILLER AVENUE     FULTON STREET   
2075426  (40.706512, -73.878136)            EDSALL AVENUE         73 STREET   

                 OFF STREET NAME  ...  CONTRIBUTING FACTOR VEHICLE 2  \
0                            NaN  ...                    Unspecified   
1                            NaN  ...                            NaN   
2                            NaN  ...                    Unspecified   
3        1211      LORING AVENUE  ...                            NaN   
4                            NaN  ...                            NaN   
...                          ...  ...                            ...   
2075422                      NaN  ...                    Unspecified   
2075423       161       AVENUE O  ...                    Unspecified   
2075424                      NaN  ...                    Unspecified   
2075425                      NaN  ...                            NaN   
2075426                      NaN  ...                    Unspecified   

         CONTRIBUTING FACTOR VEHICLE 3  CONTRIBUTING FACTOR VEHICLE 4  \
0                                  NaN                            NaN   
1                                  NaN                            NaN   
2                                  NaN                            NaN   
3                                  NaN                            NaN   
4                                  NaN                            NaN   
...                                ...                            ...   
2075422                            NaN                            NaN   
2075423                    Unspecified                    Unspecified   
2075424                            NaN                            NaN   
2075425                            NaN                            NaN   
2075426                            NaN                            NaN   

         CONTRIBUTING FACTOR VEHICLE 5  COLLISION_ID  \
0                                  NaN       4455765   
1                                  NaN       4513547   
2                                  NaN       4541903   
3                                  NaN       4456314   
4                                  NaN       4486609   
...                                ...           ...   
2075422                            NaN       4707511   
2075423                            NaN       4707419   
2075424                            NaN       4707855   
2075425                            NaN       4707872   
2075426                            NaN       4707447   

                         VEHICLE TYPE CODE 1  \
0                                      Sedan   
1                                      Sedan   
2                                      Sedan   
3                                      Sedan   
4                                        NaN   
...                                      ...   
2075422  Station Wagon/Sport Utility Vehicle   
2075423                            Ambulance   
2075424  Station Wagon/Sport Utility Vehicle   
2075425  Station Wagon/Sport Utility Vehicle   
2075426                                Sedan   

                         VEHICLE TYPE CODE 2  VEHICLE TYPE CODE 3  \
0                                      Sedan                  NaN   
1                                        NaN                  NaN   
2                              Pick-up Truck                  NaN   
3                                        NaN                  NaN   
4                                        NaN                  NaN   
...                                      ...                  ...   
2075422  Station Wagon/Sport Utility Vehicle                  NaN   
2075423                                   PK                  Van   
2075424                                   PK                  NaN   
2075425                                  NaN                  NaN   
2075426  Station Wagon/Sport Utility Vehicle                  NaN   

        VEHICLE TYPE CODE 4 VEHICLE TYPE CODE 5  
0                       NaN                 NaN  
1                       NaN                 NaN  
2                       NaN                 NaN  
3                       NaN                 NaN  
4                       NaN                 NaN  
...                     ...                 ...  
2075422                 NaN                 NaN  
2075423                  PK                 NaN  
2075424                 NaN                 NaN  
2075425                 NaN                 NaN  
2075426                 NaN                 NaN  

[2075427 rows x 29 columns]
In [3]:
profile = ProfileReport(df, title = "New York Data Report")
In [4]:
profile.to_notebook_iframe()
Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]
Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]
Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]
In [ ]: